Computing an accurate radio navigation-based position solution in challenging signal environments such as urban canyons and areas of dense foliage can be difficult. In these challenging signal environments, fewer signals are available, and those signals that are available tend to yield less accurate measurements on a device due to environmental attenuation. One approach to improving the availability and quality of position solutions in challenging signal environments is to combine observations of radio navigation signals with input from other sensors or signals that measure some aspect of user or antenna motion between or during the measurement of radio navigation signals. The additional information improves the position solution by subtracting out antenna motion between epochs of radio navigation measurements, effectively allowing multiple epochs of measurements to be statistically combined to reduce error.
One approach to improving the availability and quality of position solutions blends measurements of radio navigation signals in a Kalman Filter with numerical integration of accelerometer and/or rate gyroscope measurements or the like to correct for antenna motion between epochs. For this approach, the numerical integration component is often called an inertial navigation system (INS) or DR component. In this approach, the DR component is used to subtract antenna motion between epochs so multiple epochs of radio navigation measurements may be combined. However, because the DR component estimates motion from one epoch to the next, the DR component accumulates errors over time as that motion is combined over multiple epochs.
It is desirable in pedestrian motion estimation to minimize accumulated pedestrian motion errors by making the DR component more accurate. One way to do this is to select a motion model that allocates adequate degrees of freedom to model the object undergoing motion without assigning unnecessary degrees of freedom. A typical motion model that does a poor job at this task might assign a constant velocity to a user regardless of the motion activity class (e.g., driving, walking). Assigning a constant velocity will result in poor DR performance when, for example, a walking pedestrian with swinging arms holds the device (and hence its sensors) in hand. In this case, the motion of the device is not equal to the motion of the user. In fact, the device may at times move twice as fast as the user. At other times, the device may appear stationary while the user is walking.